Romain MAILLOT RomainMaillot13

## gist:48b8200d56c9a621043561146478765f
<html>
  <body>
    <!-- Load TensorFlow.js -->
    <script src="https://unpkg.com/@tensorflow/tfjs"></script>
    <!-- Load Posenet -->
    <script src="https://unpkg.com/@tensorflow-models/posenet">
    </script>
    <script type="text/javascript">
      posenet.load().then(function(net) {
        // posenet model loaded

## Library
<html>
  <body>
    <!-- Load TensorFlow.js -->
    <script src="https://unpkg.com/@tensorflow/tfjs"></script>
    <!-- Load Posenet -->
    <script src="https://unpkg.com/@tensorflow-models/posenet">
    </script>
    <script type="text/javascript">
      posenet.load().then(function(net) {
        // posenet model loaded

## Video detection
let classifier;
let video;

function setup() {
  noCanvas();
  // Create a camera input
  video = createCapture(VIDEO);
  // Initialize the Image Classifier method with MobileNet and the video as the second argument
  classifier = ml5.imageClassifier('MobileNet', video, modelReady);
}

## dataset
idenprof//train//Lila// 900 images of Lila
idenprof//train//Marc// 900 images of Marc
idenprof//train//Rob// 900 images of Rob
idenprof//test//Lila// 200 images of Lila
idenprof//test//Marc// 200 images of Marc
idenprof//test//Rob// 200 images of Rob

## Algorithm one person
const imageScaleFactor = 0.50;
const flipHorizontal = false;
const outputStride = 16;
const imageElement = document.getElementById('cat');
// load the posenet model
const net = await posenet.load();
const pose = await net.estimateSinglePose(imageElement, scaleFactor, flipHorizontal, outputStride);

## Algorithm several persons
const imageScaleFactor = 0.50;
const flipHorizontal = false;
const outputStride = 16;
// get up to 5 poses
const maxPoseDetections = 5;
// minimum confidence of the root part of a pose
const scoreThreshold = 0.5;
// minimum distance in pixels between the root parts of poses
const nmsRadius = 20;
const imageElement = document.getElementById('cat');

## facebook login

// Add this to the header of your file, e.g. in ViewController.m
// after #import "ViewController.h"
#import <FBSDKCoreKit/FBSDKCoreKit.h>
#import <FBSDKLoginKit/FBSDKLoginKit.h>

// Add this to the body
@implementation ViewController

- (void)viewDidLoad {

## api messenger
"messaging_type": "<MESSAGING_TYPE>",
"recipient":{
  "id":"<PSID>"
},
"message":{
  "text":"hello, world!"
}

## get location
<script>
function maPosition(position) {
  var infopos = "Position déterminée :\n";
  infopos += "Latitude : "+position.coords.latitude +"\n";
  infopos += "Longitude: "+position.coords.longitude+"\n";
  infopos += "Altitude : "+position.coords.altitude +"\n";
  document.getElementById("infoposition").innerHTML = infopos;
}

if(navigator.geolocation)

## connect to google map api
function maPosition(position) {

    var infopos = "Position déterminée :\n";
    infopos += "Latitude : "+position.coords.latitude +"\n";
    infopos += "Longitude: "+position.coords.longitude+"\n";
    infopos += "Altitude : "+position.coords.altitude +"\n";
    infopos += "Vitesse  : "+position.coords.speed +"\n";
    document.getElementById("infoposition").innerHTML = infopos;

    // Un nouvel objet LatLng pour Google Maps avec les paramètres de position
	<html>
	<body>
	<!-- Load TensorFlow.js -->
	<script src="https://unpkg.com/@tensorflow/tfjs"></script>
	<!-- Load Posenet -->
	<script src="https://unpkg.com/@tensorflow-models/posenet">
	</script>
	<script type="text/javascript">
	posenet.load().then(function(net) {
	// posenet model loaded
	let classifier;
	let video;

	function setup() {
	noCanvas();
	// Create a camera input
	video = createCapture(VIDEO);
	// Initialize the Image Classifier method with MobileNet and the video as the second argument
	classifier = ml5.imageClassifier('MobileNet', video, modelReady);
	}
	idenprof//train//Lila// 900 images of Lila
	idenprof//train//Marc// 900 images of Marc
	idenprof//train//Rob// 900 images of Rob
	idenprof//test//Lila// 200 images of Lila
	idenprof//test//Marc// 200 images of Marc
	idenprof//test//Rob// 200 images of Rob
	const imageScaleFactor = 0.50;
	const flipHorizontal = false;
	const outputStride = 16;
	const imageElement = document.getElementById('cat');
	// load the posenet model
	const net = await posenet.load();
	const pose = await net.estimateSinglePose(imageElement, scaleFactor, flipHorizontal, outputStride);

	// Add this to the header of your file, e.g. in ViewController.m
	// after #import "ViewController.h"
	#import <FBSDKCoreKit/FBSDKCoreKit.h>
	#import <FBSDKLoginKit/FBSDKLoginKit.h>

	// Add this to the body
	@implementation ViewController

	- (void)viewDidLoad {
	"messaging_type": "<MESSAGING_TYPE>",
	"recipient":{
	"id":"<PSID>"
	},
	"message":{
	"text":"hello, world!"
	}
	<script>
	function maPosition(position) {
	var infopos = "Position déterminée :\n";
	infopos += "Latitude : "+position.coords.latitude +"\n";
	infopos += "Longitude: "+position.coords.longitude+"\n";
	infopos += "Altitude : "+position.coords.altitude +"\n";
	document.getElementById("infoposition").innerHTML = infopos;
	}

	if(navigator.geolocation)